The analysis of games and game-playing has long been a mainstay of research in the field of artificial intelligence (AI). From the first development of game theory by Morgenstern and von Neumann and Samuels’ creation of a program to play the game of draughts at a level sufficient to challenge a human to the present day, much work has been undertaken to improve the ability of a computer to comprehend, analyse and play a game at a human level. Much of this work has been in a class of games readily tractable to game-theoretical approaches, such as chequers, Othello and chess; many other games exist which by nature or design resist standard AI techniques. However, in 2011 came a demonstration of a computer agent not only playing but winning – and winning resoundingly – at such a game: the game was the renowned quiz show Jeopardy!, and the computer was IBM’s ‘cognitive computer’, Watson.

The general method of game-playing in AI is to generate a move tree, representing each move and its possible subsequents in the computer’s memory, and score this as the tree is developed; the child node of the root with the highest overall score thus represents the ‘best’ or ‘optimal’ next move. In contrast to this, Watson uses a set of small analytics systems, which IBM calls ‘annotators’, to add separate pieces of metadata to a common data structure as it passes down a process pipeline. Each annotator may be called as required if and when new data are presented, and as the analysis continues further sets of data are added. At the end of the pipeline there may additionally be a machine learning (ML) system, neural net or other subsystem which implements a form of ‘artificial intuition’. This modular approach, in which multiple sub-components add information to the dataset for further analysis later, was fundamental to Watson winning Jeopardy! in 2011 and also provides an excellent opportunity to create much more varied and subtle computer opponents.

I present herein an alternative approach from ‘traditional’ game AI, derived from the concepts which underpin Watson’s technology, and demonstrate its usefulness in creating agents which can both play more complex, less game-theoretical, games and use a simple strategy to augment their move selection. This is the first tabletop (board, card, or role-playing) game AI to use the concepts and methods of cognitive computing in general and Watson in particular. The system will be demonstrated using the game Infinite City, published by Alderac Entertainment Group, a tile-based game in which players attempt to control sections of a ‘city’ represented by those tiles. Infinite City is highly mutable and can rapidly become combinatorially explosive, rendering it significantly less amenable to traditional game search methods.